Gemini 3.1 Pro vs GPT-5.4: Full 2026 Comparison
Gemini 3.1 Pro vs GPT-5.4: Full Comparison
The two most powerful general-purpose AI models of early 2026, compared across every dimension that matters. Google’s adjustable thinking approach vs OpenAI’s model variant strategy — which is right for your use case?
Last verified: March 2026
Quick Comparison
| Feature | Gemini 3.1 Pro | GPT-5.4 |
|---|---|---|
| Developer | Google DeepMind | OpenAI |
| Released | Feb 19, 2026 | Jan 2026 |
| Context | 1M tokens | 1M tokens |
| Flexibility | 3 thinking levels | 3 model variants |
| Multimodal | Text, image, video, audio | Text, image, audio |
| Native video | Yes | No |
| Cheapest tier | $0.50/$1.50 per 1M tok | $0.15/$0.60 per 1M tok (Nano) |
| Top tier | $2.50/$10.00 per 1M tok | $2.50/$10.00 per 1M tok |
Architecture: Thinking Levels vs Model Variants
Gemini 3.1 Pro: One Model, Three Modes
You choose the thinking level per request:
- LOW — Fast, cheap, good for simple tasks
- MEDIUM — Balanced reasoning for everyday work
- HIGH — Deep chain-of-thought for hard problems
The advantage: seamless switching within a conversation. Start LOW for context gathering, switch to HIGH for analysis, back to LOW for formatting output.
GPT-5.4: Three Separate Models
OpenAI offers distinct models:
- GPT-5.4 — Full capability flagship
- GPT-5.4 Mini — Balanced performance and cost
- GPT-5.4 Nano — Fastest, cheapest, limited reasoning
The advantage: each variant is independently optimized for its tier, potentially offering better performance at each price point than a single model switching modes.
Benchmark Comparison
| Benchmark | Gemini 3.1 Pro (HIGH) | GPT-5.4 |
|---|---|---|
| MMLU-Pro | 90.2% | 91.3% |
| HumanEval | 92.8% | 95.1% |
| MATH | 88.4% | 89.7% |
| GPQA | 71.3% | 73.8% |
| BrowseComp | 77.1% | 81.2% |
| Vision (MMMU) | 78.9% | 78.4% |
GPT-5.4 leads on most text-based benchmarks. Gemini 3.1 Pro is competitive on vision tasks and edges ahead on video understanding (not benchmarked above).
Coding Comparison
| Task | Gemini 3.1 Pro | GPT-5.4 |
|---|---|---|
| Code generation | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ |
| Debugging | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ |
| Code review | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ |
| Multi-file refactoring | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ |
| Rapid prototyping | ⭐⭐⭐⭐ (LOW mode) | ⭐⭐⭐⭐ (Nano) |
GPT-5.4 maintains an edge in coding, particularly for complex multi-file tasks. Gemini 3.1 Pro is strong but slightly behind.
Pricing Deep Dive
| Use Case | Gemini 3.1 Pro | GPT-5.4 |
|---|---|---|
| Simple queries | $0.50/$1.50 (LOW) | $0.15/$0.60 (Nano) |
| General tasks | $1.25/$5.00 (MEDIUM) | $1.25/$5.00 (Mini) |
| Complex reasoning | $2.50/$10.00 (HIGH) | $2.50/$10.00 (Full) |
Pricing is surprisingly similar at equivalent tiers. GPT-5.4 Nano is cheaper than Gemini LOW for simple tasks. For mixed workloads, the cost difference is minimal.
Multimodal Capabilities
Gemini 3.1 Pro Advantages
- Native video understanding — Process video directly without frame extraction
- Audio processing — Native speech and audio understanding
- Google ecosystem — Deep integration with Workspace, Search, Cloud
GPT-5.4 Advantages
- Image generation — DALL-E integration for image creation
- Plugin ecosystem — Larger third-party tool ecosystem
- Function calling — More mature tool-use capabilities
When to Choose Each
Choose Gemini 3.1 Pro if:
- You need video understanding
- Cost optimization matters (adjustable thinking saves money)
- You’re in the Google Cloud ecosystem
- You want per-request flexibility without switching models
Choose GPT-5.4 if:
- Maximum coding capability is critical
- You need the strongest possible reasoning
- You’re building with OpenAI’s tool ecosystem
- You want separate, purpose-optimized model tiers
Verdict
GPT-5.4 wins on raw capability by a small margin. Gemini 3.1 Pro wins on flexibility and native multimodal breadth. For most applications, either model will serve you well. The choice often comes down to ecosystem preference — Google Cloud vs OpenAI API — rather than model capability differences.
Last verified: March 2026